Sbir phase i: autonomous interferometric synthetic aperture radar (insar) for surface deformation monitoring

This National Science Foundation Project Grant of $254,707 will fund the development of machine learning and artificial intelligence methods to automate and accelerate the processing of interferometric synthetic aperture radar (InSAR) time series data. The grant was awarded to Geolabe LLC on March 1, 2023 under the NSF Technology, Innovation, and Partnerships program (CFDA 47.084).

Specifically, the company will work to further automate key sections of the InSAR processing pipeline that currently rely on extensive human intervention. It will also develop a new methodology to generate InSAR time series data that is more robust to noise and allows for finer temporal and spatial resolution compared to current state-of-the-art techniques. The improved automation and noise reduction enabled by this research could enhance global monitoring of surface deformation related to natural hazards, infrastructure integrity, resource extraction, and other applications by facilitating large-scale, autonomous InSAR analysis.

Abstract

The broader impact of this small business innovation research (sbir) phase i project is to enable the global autonomous detection of surface deformation. Measuring earth surface deformation is fundamental to detect and analyze surface and subsurface changes due to anthropogenic activity, with a myriad of industrial applications that includes the monitoring of oil and gas extraction fields and storage reservoirs, mining operations, carbon dioxide sequestration, and/or infrastructure integrity. Illustrating the economic and social impact of its uses, the market for analyzing interferometric synthetic aperture radar (insar) data is expected to double within 5 years. Beyond the dramatic economic growth of insar, its far-ranging applications have broad social and scientific impacts, in particular related to natural hazards and climate change. Advances in insar processing and improved signal-to-noise ratios will translate into improved monitoring of earthquake activity, landslides, water supplies, deforestation, floods, ICE sheets, etc. This small business innovation research (sbir) phase i project aims at tackling the lack of automation in insar processing and improving detection thresholds in insar time series analysis. While the technique can potentially measure millimeter-scale changes in deformation over periods of days to years, atmospheric effects can wreak havoc on repeat-pass insar interpretation by introducing errors that may mask small surface deformations. These effects, which are fundamentally due to pressure, temperature and relative humidity variations in the troposphere, can lead to errors that are larger than most of the deformation signals of interest. Current algorithms are not suited for automated, large-scale monitoring without a priori data because they require time-consuming manual intervention, and the final product requires exhaustive expert interpretation. Through the development of machine learning and artificial intelligence methods this project aims at: (i) further automating and accelerating the processing of insar time series, via the automation of some key sections of the processing pipeline that still rely on extensive and costly human intervention; and (II) developing a new methodology to generate insar time series, that is robust to noise and allows for a finer temporal and spatial resolution compared to the state-of-the-art. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the foundation's intellectual merit and broader impacts review criteria.